Xiahui Li
Ph.D. Candidate in Statistics | University of St Andrews
I am a PhD candidate in the School of Mathematics and Statistics at the University of St Andrews. I hold a Master's degree in Statistics from the University of Nottingham and a Bachelor's degree in Statistics from Queen's University, Canada. My research focuses on advancing approximate Bayesian inference methods and their applications in epidemiology and ecology.
My work addresses the computational challenges inherent in likelihood-free inference for complex dynamic systems. My research interests include computational statistics, Bayesian inference, epidemiological modeling, and statistical methodology for complex stochastic systems.
Publications
Advances in Approximate Bayesian Inference for Models in Epidemiology
Epidemics, 2025
This review synthesizes recent advances in approximate Bayesian methods that prioritize both computational efficiency and inferential accuracy. We evaluate four key families—Approximate Bayesian Computation, Bayesian Synthetic Likelihood, Integrated Nested Laplace Approximation, and Variational Inference—and provide practical guidance for their application in epidemiology.
A Novel Approximate Bayesian Inference Method for Compartmental Models in Epidemiology using Stan
arXiv preprint, 2024
We develop a method that employs entropy criterion to select the most informative subset of summary statistics, which are then used to construct a synthetic likelihood for posterior sampling. Posterior sampling is performed using Hamiltonian Monte Carlo as implemented in the Stan software.
Actionable Guidelines for Promoting Equity, Diversity and Inclusion in Infectious Disease Modelling
Epidemics, 2025 (Under Review)
This paper identifies key challenges of promoting EDI in IDD community and proposes actionable guidelines to foster inclusivity, collaboration and innovation in the field. Unlike generic EDI guidelines, we provide context-specific recommendations for three key settings in infectious disease research: team dynamics, conferences and virtual collaborations.
Infectious Disease Outbreak Controllability: Biological, Social and Public Health Factors
Proceedings of The Royal Society B, 2025 (Under Review)
This work identifies and categorizes the critical factors affecting outbreak control. We provide a structured framework, analyzing elements related to the pathogen, host population, and available interventions to guide policy assessment.
A Workflow for Infectious Disease Modelling
arXiv preprint, 2025 (Under Submission)
We suggest a workflow for developing and evaluating infectious disease models, building on general Bayesian workflow advice and focusing on domain-specific challenges. This workflow is designed for anyone developing an infectious disease model, and for users of model outputs who need to be able to evaluate modelling studies.
Teaching
MT 1001: Introductory Mathematics
Fall 2023This module provides a rigorous foundation in single-variable calculus, including limits, derivatives, and integrals. The focus is on building the mathematical skills necessary to model and solve problems in physics, engineering, and other scientific disciplines.
MT 2508: Statistical Inference
Spring 2024 & 2025This module explores how we use probability models to quantify uncertainty and make inferences from data. Students learn key statistical methods, including maximum likelihood estimation, confidence intervals, hypothesis testing, and linear regression, forming the essential foundation for advanced statistical study.
MT 2501: Linear Mathematics
Fall 2024This module builds on matrices and linear systems to explore the fundamental structures of linear algebra: vector spaces, linear independence, transformations, and diagonalization, with applications across the mathematical sciences.
MT 4113: Computing in Statistics
Fall 2023, 2024 & 2025This module builds practical programming skills for statistical analysis. Students learn to write efficient, modular R code for data manipulation, simulation, and investigating statistical procedures.
Summer Team Enterprise Program Coach
Summer 2024Coached a team of undergraduates through exploratory data analysis of oceanographic datasets, from research question formulation to final reporting. Their insights directly contributed to the development of the new module, "The History and Future of Data."
Presentations
Conference & Workshop Talks
Invited Seminars
Curriculum Vitae
Education
Supervisors: Dr Ben Swallow and Dr Fergus Chadwick
Supervisors: Dr Christopher Fallaize and Dr Riccardo Corradin
Research Activities
